
## Setup
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore', DeprecationWarning)
%matplotlib inline
import seaborn as sns
import plotly.express as px

from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.preprocessing import StandardScaler
from matplotlib.pyplot import scatter
import plotly
from plotly.graph_objs import Scatter, Marker, Layout, layout,XAxis, YAxis, Bar, Line
%matplotlib inline

##Decision tree setup
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix


# load the airline satisfaction  dataset

df = pd.read_csv('https://raw.githubusercontent.com/dk28yip/MSDS7331/main/airline.csv') # read in the csv file
df.head()
#reduced samples set from 100,000 to 30,000 as a few of us had computer performance issues
df = df.sample(n=30000)


# Any missing values in the dataset
def plot_missingness(df: pd.DataFrame=df) -> None:
    nan_df = pd.DataFrame(df.isna().sum()).reset_index()
    nan_df.columns  = ['Column', 'NaN_Count']
    nan_df['NaN_Count'] = nan_df['NaN_Count'].astype('int')
    nan_df['NaN_%'] = round(nan_df['NaN_Count']/df.shape[0] * 100,4)
    nan_df['Type']  = 'Missingness'
    nan_df.sort_values('NaN_%', inplace=True)

    # Add completeness
    for i in range(nan_df.shape[0]):
        complete_df = pd.DataFrame([nan_df.loc[i,'Column'],df.shape[0] - nan_df.loc[i,'NaN_Count'],100 - nan_df.loc[i,'NaN_%'], 'Completeness']).T
        complete_df.columns  = ['Column','NaN_Count','NaN_%','Type']
        complete_df['NaN_%'] = complete_df['NaN_%'].astype('int')
        complete_df['NaN_Count'] = complete_df['NaN_Count'].astype('int')
        nan_df = pd.concat([nan_df,complete_df], sort=True)
            
    nan_df = nan_df.rename(columns={"Column": "Feature", "NaN_%": "Missing %"})

    # Missingness Plot
    fig = px.bar(nan_df,
                 x='Feature',
                 y='Missing %',
                 title=f"Missingness Plot (N={df.shape[0]})",
                 color='Type',
                 opacity = 0.6,
                 color_discrete_sequence=['red','#808080'],
                 width=800,
                 height=800)
    fig.show()

plot_missingness(df)

print("Missing 99 values if the 'Arrival Delay in Minutes'column; approximately 0.31%.")

Missing 99 values if the 'Arrival Delay in Minutes'column; approximately 0.31%.


df["GenderNumeric"] = (df["Gender"]=="Male").astype(int)
df["CustomerTypeNumeric"] = (df["Customer Type"]=="Loyal Customer").astype(int)
df["TypeofTravelNumeric"] = (df["Type of Travel"]=="Personal Travel").astype(int)
df["ClassNumeric"] = df["Class"]
df["ClassNumeric"].replace(['Eco', 'Eco Plus', 'Business'], [0, 1, 2], inplace=True)

df["Arrival Delay in Minutes"]= df["Arrival Delay in Minutes"].fillna(0)


dfclean = df.drop(columns=['id'])

dfclean.isnull().sum() #double check on the missing values - 'arrival delay in minutes =310'

Gender                               0
Customer Type                        0
Age                                  0
Type of Travel                       0
Class                                0
Flight Distance                      0
Inflight wifi service                0
Departure/Arrival time convenient    0
Ease of Online booking               0
Gate location                        0
Food and drink                       0
Online boarding                      0
Seat comfort                         0
Inflight entertainment               0
On-board service                     0
Leg room service                     0
Baggage handling                     0
Checkin service                      0
Inflight service                     0
Cleanliness                          0
Departure Delay in Minutes           0
Arrival Delay in Minutes             0
satisfaction                         0
GenderNumeric                        0
CustomerTypeNumeric                  0
TypeofTravelNumeric                  0
ClassNumeric                         0
dtype: int64


#Fill in missing values
dfclean["Arrival Delay in Minutes"].fillna(dfclean["Arrival Delay in Minutes"].median(), inplace=True)


dfclean.describe().T


dfclean.corr()


f, ax = plt.subplots(figsize=[18, 13])
sns.heatmap(dfclean.corr(), annot=True, fmt=".2f", ax=ax, cmap="bwr")
ax.set_title("Correlation Matrix", fontsize=20)
plt.show()


##distribution of the data

for column in dfclean:
    
    sns.displot(x=column, data=dfclean)

C:\Datascience\Anaconda3\envs\ML7331\lib\site-packages\seaborn\axisgrid.py:409: RuntimeWarning:

More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).


print (dfclean.info())

<class 'pandas.core.frame.DataFrame'>
Int64Index: 30000 entries, 15146 to 77455
Data columns (total 27 columns):
 #   Column                             Non-Null Count  Dtype  
---  ------                             --------------  -----  
 0   Gender                             30000 non-null  object 
 1   Customer Type                      30000 non-null  object 
 2   Age                                30000 non-null  int64  
 3   Type of Travel                     30000 non-null  object 
 4   Class                              30000 non-null  object 
 5   Flight Distance                    30000 non-null  int64  
 6   Inflight wifi service              30000 non-null  int64  
 7   Departure/Arrival time convenient  30000 non-null  int64  
 8   Ease of Online booking             30000 non-null  int64  
 9   Gate location                      30000 non-null  int64  
 10  Food and drink                     30000 non-null  int64  
 11  Online boarding                    30000 non-null  int64  
 12  Seat comfort                       30000 non-null  int64  
 13  Inflight entertainment             30000 non-null  int64  
 14  On-board service                   30000 non-null  int64  
 15  Leg room service                   30000 non-null  int64  
 16  Baggage handling                   30000 non-null  int64  
 17  Checkin service                    30000 non-null  int64  
 18  Inflight service                   30000 non-null  int64  
 19  Cleanliness                        30000 non-null  int64  
 20  Departure Delay in Minutes         30000 non-null  int64  
 21  Arrival Delay in Minutes           30000 non-null  float64
 22  satisfaction                       30000 non-null  object 
 23  GenderNumeric                      30000 non-null  int32  
 24  CustomerTypeNumeric                30000 non-null  int32  
 25  TypeofTravelNumeric                30000 non-null  int32  
 26  ClassNumeric                       30000 non-null  int64  
dtypes: float64(1), int32(3), int64(18), object(5)
memory usage: 7.1+ MB
None


print(dfclean["satisfaction"].value_counts())

fig = plt.figure(figsize=(10, 5))
dfclean.groupby('satisfaction').size().plot(kind='pie',
                                       y = "satisfaction",
                                       label = "Type",
                                       autopct='%1.0f%%')

neutral or dissatisfied    17045
satisfied                  12955
Name: satisfaction, dtype: int64

<AxesSubplot:ylabel='Type'>


#chang output to 1-Satisfied and 0-neutral/unsatisfied
dfclean["satisfaction"] = dfclean["satisfaction"].apply(lambda x: 1 if x == "satisfied" else 0)


from numpy import mean
from numpy import std
from sklearn.model_selection import cross_val_score


from sklearn.model_selection import RepeatedStratifiedKFold
x = dfclean.drop(["satisfaction", "Class", "Gender", "Customer Type", "Type of Travel"],axis=1)
y = dfclean['satisfaction']
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=10, random_state=1)


from sklearn.linear_model import LogisticRegression
from sklearn import metrics as mt

lr_clf = LogisticRegression(penalty='l2', C=1, class_weight=None, solver='liblinear' ) # get object

#calculate logistic regression model average f1 scores of cross validation
lr_scores = cross_val_score(lr_clf, x, y, cv=cv)
print('Logsitics Regression of F1 Score of repeated (10 times) 10-fold cross validation: %.3f (%.3f)' % (mean(lr_scores), std(lr_scores)), "\n\n")

Logsitics Regression of F1 Score of repeated (10 times) 10-fold cross validation: 0.876 (0.006)


# check how C changes with accruacy prediction for logistic regression
from sklearn.metrics import accuracy_score

scl_obj = StandardScaler()
scl_obj.fit(x)

x_scaled = scl_obj.transform(x)
x_train, x_test, y_train, y_test = train_test_split(x_scaled, y, test_size=.2)


accuracy, params = [], []
for c in np.arange(1, 20):
    log_linear = LogisticRegression(penalty='l2', C= c*0.1, class_weight=None, solver='liblinear')
    log_linear.fit(x_train,y_train)
    y_hat = log_linear.predict(x_test)
    
    accuracy.append(accuracy_score(y_test, y_hat))
    params.append(c)
accuracy = np.array(accuracy)
plt.plot(params, accuracy)
plt.ylabel('accuracy of prediction')
plt.xlabel('C')
plt.legend(loc='upper left')
#plt.xscale('log')
plt.show()

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.


from sklearn.model_selection import GridSearchCV

parameters = {'C':[1, 2, 5, 10, 20, 50]}
log_reg_model = LogisticRegression(max_iter=50000,penalty='l2',class_weight=None,solver='liblinear')
cv_grid = GridSearchCV(log_reg_model, parameters)
cv_grid.fit(x_train, y_train)
cv_grid.best_params_

{'C': 1}


from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
DT_model = DecisionTreeClassifier()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2)


#calculate decision tree model average f1 scores of cross validation
DT_scores = cross_val_score(DT_model, x, y, scoring='f1_macro', cv=cv)
print('Decision Tree F1 Score of repeated (10 times) 10-fold cross validation: %.3f (%.3f)' % (mean(DT_scores), std(DT_scores)), "\n\n")

Decision Tree F1 Score of repeated (10 times) 10-fold cross validation: 0.935 (0.005)


from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
KNN_model = KNeighborsClassifier(n_neighbors=5)


x_scaled = x
scaler = StandardScaler()
scaler.fit(x_scaled)
x_scaled = scaler.transform(x_scaled)


knn_scores = []
for i in list(range(1,10)):
    knn_loop_model = KNeighborsClassifier(n_neighbors=i)
    scores = cross_val_score(knn_loop_model, x_scaled, y, scoring='f1_macro', cv=cv)
    knn_scores.append(mean(scores))


#graph the f1 scores of the different knns
sns.set()
k_scores = pd.DataFrame()
k_scores['k'] = list(range(1, 10))
k_scores['score'] = knn_scores
sns.scatterplot(data=k_scores, x='k', y='score').set(title='K Values and Scores')

[Text(0.5, 1.0, 'K Values and Scores')]


#calculate knn model average f1 scores of cross validation
knn_scores = cross_val_score(KNN_model, x_scaled, y, scoring='f1_macro', cv=cv)
print('F1 Score: %.3f (%.3f)' % (mean(knn_scores), std(knn_scores)))

F1 Score: 0.919 (0.005)


import math

#count total samples
dfclean_count=len(dfclean)

#1.96 is the z-score at a 95% CI interval

#Logisitic Regression 95% Confidence Interval
errorLR = 1 - mean(lr_scores)
varLR = ((1-errorLR) * errorLR)/dfclean_count
sqrtNumLR = math.sqrt(varLR)
LRUpper = (errorLR) + (1.96 * sqrtNumLR)
LRLower =(errorLR) - (1.96 * sqrtNumLR)

#KNN 95% Confidence Interval
errorKNN = 1 - mean(knn_scores)
varKNN = ((1-errorKNN) * errorKNN)/dfclean_count
sqrtNumKNN = math.sqrt(varKNN)
KNNUpper = (errorKNN) + (1.96 * sqrtNumKNN)
KNNLower =(errorKNN) - (1.96 * sqrtNumKNN)

#Decision tree 95% Confidence Interval
errorDT = 1 - mean(DT_scores) 
varDT = ((1-errorDT) * errorDT)/dfclean_count
sqrtNumDT = math.sqrt(varDT)
DTUpper = (errorDT) + (1.96 * sqrtNumDT)
DTLower =(errorDT) - (1.96 * sqrtNumDT)

print('1) Decision Tree 95% Confident Interval: ',DTUpper, DTLower)
print('2) KNN 95% Confident Interval: ',KNNUpper, KNNLower)
print('3) Logistic Regression 95% Confident Interval: ',LRUpper, LRLower)

1) Decision Tree 95% Confident Interval:  0.06732967855094532 0.06176832222570802
2) KNN 95% Confident Interval:  0.08453001396923618 0.07834007951360225
3) Logistic Regression 95% Confident Interval:  0.127776835405402 0.12031649792793106


dt_clf = DecisionTreeClassifier()

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2)

dt_clf.fit(x_train,y_train)
yhat = dt_clf.predict(x_test)
print ('accuracy:', mt.accuracy_score(y_test,yhat))

# get the importances
imp = dt_clf.feature_importances_

#print out importat features
DT_important = dt_clf.feature_importances_
for i,v in enumerate(DT_important):
	print('Feature: %0d, Score: %.5f' % (i,v))

accuracy: 0.9338333333333333
Feature: 0, Score: 0.02840
Feature: 1, Score: 0.02195
Feature: 2, Score: 0.18038
Feature: 3, Score: 0.00510
Feature: 4, Score: 0.00419
Feature: 5, Score: 0.01455
Feature: 6, Score: 0.00498
Feature: 7, Score: 0.35105
Feature: 8, Score: 0.01347
Feature: 9, Score: 0.04714
Feature: 10, Score: 0.00997
Feature: 11, Score: 0.01219
Feature: 12, Score: 0.01672
Feature: 13, Score: 0.03428
Feature: 14, Score: 0.01472
Feature: 15, Score: 0.01453
Feature: 16, Score: 0.00512
Feature: 17, Score: 0.00865
Feature: 18, Score: 0.00473
Feature: 19, Score: 0.03746
Feature: 20, Score: 0.14977
Feature: 21, Score: 0.02065

	count	mean	std	min	25%	50%	75%	max
Age	30000.0	39.333333	15.154087	7.0	27.0	40.0	51.0	85.0
Flight Distance	30000.0	1185.172233	996.070790	31.0	414.0	836.5	1726.0	4983.0
Inflight wifi service	30000.0	2.732333	1.326479	0.0	2.0	3.0	4.0	5.0
Departure/Arrival time convenient	30000.0	3.061733	1.523133	0.0	2.0	3.0	4.0	5.0
Ease of Online booking	30000.0	2.745933	1.394506	0.0	2.0	3.0	4.0	5.0
Gate location	30000.0	2.967167	1.272874	1.0	2.0	3.0	4.0	5.0
Food and drink	30000.0	3.207767	1.325993	0.0	2.0	3.0	4.0	5.0
Online boarding	30000.0	3.256067	1.346263	0.0	2.0	3.0	4.0	5.0
Seat comfort	30000.0	3.439733	1.317887	1.0	2.0	4.0	5.0	5.0
Inflight entertainment	30000.0	3.356600	1.332377	0.0	2.0	4.0	4.0	5.0
On-board service	30000.0	3.393267	1.291664	1.0	2.0	4.0	4.0	5.0
Leg room service	30000.0	3.342067	1.316098	0.0	2.0	4.0	4.0	5.0
Baggage handling	30000.0	3.625367	1.190461	1.0	3.0	4.0	5.0	5.0
Checkin service	30000.0	3.305067	1.265512	1.0	3.0	3.0	4.0	5.0
Inflight service	30000.0	3.643867	1.175875	1.0	3.0	4.0	5.0	5.0
Cleanliness	30000.0	3.293233	1.312036	0.0	2.0	3.0	4.0	5.0
Departure Delay in Minutes	30000.0	14.674167	37.877541	0.0	0.0	0.0	12.0	978.0
Arrival Delay in Minutes	30000.0	14.978233	37.996260	0.0	0.0	0.0	13.0	970.0
GenderNumeric	30000.0	0.490367	0.499916	0.0	0.0	0.0	1.0	1.0
CustomerTypeNumeric	30000.0	0.818067	0.385796	0.0	1.0	1.0	1.0	1.0
TypeofTravelNumeric	30000.0	0.313800	0.464044	0.0	0.0	0.0	1.0	1.0
ClassNumeric	30000.0	1.023467	0.962954	0.0	0.0	1.0	2.0	2.0

	Age	Flight Distance	Inflight wifi service	Departure/Arrival time convenient	Ease of Online booking	Gate location	Food and drink	Online boarding	Seat comfort	Inflight entertainment	...	Baggage handling	Checkin service	Inflight service	Cleanliness	Departure Delay in Minutes	Arrival Delay in Minutes	GenderNumeric	CustomerTypeNumeric	TypeofTravelNumeric	ClassNumeric
Age	1.000000	0.088761	0.010898	0.035966	0.017280	-0.008419	0.022636	0.209064	0.159351	0.069824	...	-0.050521	0.033044	-0.059903	0.056137	-0.010983	-0.013793	0.009616	0.276444	-0.045820	0.134797
Flight Distance	0.088761	1.000000	-0.000179	-0.028230	0.059409	0.006073	0.062344	0.210604	0.154774	0.128284	...	0.065263	0.072926	0.053089	0.092888	0.007348	0.004873	0.002458	0.225608	-0.265108	0.451733
Inflight wifi service	0.010898	-0.000179	1.000000	0.347462	0.717276	0.331487	0.134376	0.455373	0.124499	0.209159	...	0.120423	0.038001	0.110281	0.136155	-0.024201	-0.025128	0.011443	0.007496	-0.099762	0.030675
Departure/Arrival time convenient	0.035966	-0.028230	0.347462	1.000000	0.439865	0.437732	0.006044	0.070013	0.011336	-0.003539	...	0.077063	0.095669	0.076152	0.017797	0.000105	-0.003089	0.014659	0.197410	0.265753	-0.102602
Ease of Online booking	0.017280	0.059409	0.717276	0.439865	1.000000	0.449523	0.037039	0.405594	0.035889	0.050755	...	0.039327	0.012339	0.035587	0.022064	-0.010817	-0.012432	0.008634	0.015385	-0.122766	0.101451
Gate location	-0.008419	0.006073	0.331487	0.437732	0.449523	1.000000	-0.003463	0.001483	-0.006495	-0.002177	...	-0.002530	-0.034383	-0.007300	-0.005852	0.006564	0.005248	-0.008748	-0.004155	-0.025673	0.002478
Food and drink	0.022636	0.062344	0.134376	0.006044	0.037039	-0.003463	1.000000	0.237150	0.575546	0.622686	...	0.031572	0.083065	0.033219	0.654814	-0.034701	-0.038656	0.005760	0.058515	-0.067930	0.084525
Online boarding	0.209064	0.210604	0.455373	0.070013	0.405594	0.001483	0.237150	1.000000	0.421982	0.283006	...	0.081116	0.191832	0.069611	0.328474	-0.034074	-0.036619	-0.036949	0.190336	-0.224619	0.322052
Seat comfort	0.159351	0.154774	0.124499	0.011336	0.035889	-0.006495	0.575546	0.421982	1.000000	0.604920	...	0.073030	0.184772	0.067718	0.673116	-0.033966	-0.036365	-0.026255	0.160240	-0.122023	0.222807
Inflight entertainment	0.069824	0.128284	0.209159	-0.003539	0.050755	-0.002177	0.622686	0.283006	0.604920	1.000000	...	0.372632	0.116432	0.406043	0.691391	-0.031402	-0.035593	0.004107	0.106310	-0.151017	0.188207
On-board service	0.047301	0.099231	0.112452	0.067752	0.032247	-0.029634	0.057255	0.142793	0.125524	0.417427	...	0.514255	0.250339	0.552803	0.120820	-0.021523	-0.026300	0.010926	0.044113	-0.056015	0.197146
Leg room service	0.035710	0.127854	0.148875	0.011067	0.100626	-0.012060	0.024944	0.113150	0.100312	0.295708	...	0.373361	0.152777	0.367549	0.090902	0.009297	0.004727	0.043565	0.050027	-0.127187	0.195486
Baggage handling	-0.050521	0.065263	0.120423	0.077063	0.039327	-0.002530	0.031572	0.081116	0.073030	0.372632	...	1.000000	0.239732	0.623748	0.095028	-0.009188	-0.014576	0.041854	-0.022627	-0.027709	0.161611
Checkin service	0.033044	0.072926	0.038001	0.095669	0.012339	-0.034383	0.083065	0.191832	0.184772	0.116432	...	0.239732	1.000000	0.240392	0.173525	-0.021182	-0.024776	0.011548	0.035507	0.023392	0.150809
Inflight service	-0.059903	0.053089	0.110281	0.076152	0.035587	-0.007300	0.033219	0.069611	0.067718	0.406043	...	0.623748	0.240392	1.000000	0.088736	-0.052682	-0.058853	0.043555	-0.026731	-0.021220	0.147718
Cleanliness	0.056137	0.092888	0.136155	0.017797	0.022064	-0.005852	0.654814	0.328474	0.673116	0.691391	...	0.095028	0.173525	0.088736	1.000000	-0.018905	-0.022607	0.003672	0.083864	-0.083686	0.134758
Departure Delay in Minutes	-0.010983	0.007348	-0.024201	0.000105	-0.010817	0.006564	-0.034701	-0.034074	-0.033966	-0.031402	...	-0.009188	-0.021182	-0.052682	-0.018905	1.000000	0.958246	-0.003874	-0.007068	-0.010379	-0.010214
Arrival Delay in Minutes	-0.013793	0.004873	-0.025128	-0.003089	-0.012432	0.005248	-0.038656	-0.036619	-0.036365	-0.035593	...	-0.014576	-0.024776	-0.058853	-0.022607	0.958246	1.000000	-0.007523	-0.005648	-0.011786	-0.014097
GenderNumeric	0.009616	0.002458	0.011443	0.014659	0.008634	-0.008748	0.005760	-0.036949	-0.026255	0.004107	...	0.041854	0.011548	0.043555	0.003672	-0.003874	-0.007523	1.000000	0.029110	0.003404	0.008502
CustomerTypeNumeric	0.276444	0.225608	0.007496	0.197410	0.015385	-0.004155	0.058515	0.190336	0.160240	0.106310	...	-0.022627	0.035507	-0.026731	0.083864	-0.007068	-0.005648	0.029110	1.000000	0.311644	0.102477
TypeofTravelNumeric	-0.045820	-0.265108	-0.099762	0.265753	-0.122766	-0.025673	-0.067930	-0.224619	-0.122023	-0.151017	...	-0.027709	0.023392	-0.021220	-0.083686	-0.010379	-0.011786	0.003404	0.311644	1.000000	-0.543666
ClassNumeric	0.134797	0.451733	0.030675	-0.102602	0.101451	0.002478	0.084525	0.322052	0.222807	0.188207	...	0.161611	0.150809	0.147718	0.134758	-0.010214	-0.014097	0.008502	0.102477	-0.543666	1.000000

MSDS 7331 - Lab 2 - Airline Satisfaction Dataset¶

The dataset chosen is a compilation of airline customer satisfaction surveys. The goal is to predict customer satisfaction based on the other dataset factors.¶

Data Preparation 1 ( 10 points total) Define and prepare your class variables. Use proper variable representations (int, float, one-hot, etc.). Use pre-processing methods (as needed) for dimensionality reduction, scaling, etc. Remove variables that are not needed/useful for the analysis.¶

Check for NAs¶

Remove unwanted columns¶

Check distribution of the data¶

Data Preperation 2 (5 points total) Describe the final dataset that is used for classification/regression (include a description of any newly formed variables you created).¶

Summary of values to be used in classification modeling¶

What is the F1-measure?¶

What does it mean in our model?¶

Let's check our dataset to see if we have an unbalanced dataset¶

Method to train and test model¶

Modeling and Evaluation 4 (10 points total) Analyze the results using your chosen method of evaluation. Use visualizations of the results to bolster the analysis. Explain any visuals and analyze why they are interesting to someone that might use this model.¶

Logistic Regresion¶

Decision Tree¶

KNN¶

Logistic Regression¶

Decision tree¶

KNN¶

Features Guide¶

Exceptional Work (10 points total) You have free reign to provide additional analyses. One idea: grid search parameters in a parallelized fashion and visualize the performances across attributes. Which parameters are most significant for making a good model for each classification algorithm?¶